This paper proposes a wearable hand rehabilitation device, DULEX, for persons with functional paralysis of upper-limbs after stoke. DULEX has three degrees of freedom for rehabilitation exercises for wrist and fingers except the thumb. The main function of DULEX is to extend the range of motions of finger and wrist being contracture. DULEX is designed by using a parallel mechanism, and its parameters such as length and location of links are determined by kinematic analysis. The motion trajectory of the designed DULEX is aligned to human hand to prevent a slip. To reduce total weight of DULEX, artificial air muscles are used for actuating each joint motion. In feedback control, each joint angle is indirectly estimated from the relations of the input air pressure and the output muscle length. Experimental results show that DULEX is feasible in hand rehabilitation for stroke survivals.

Estimation methods of motion intention from bio-signal present challenges in man machine interaction(MMI) to offer user's command to machine without control of any devices. Measurements of meaningful bio-signals that contain the motion intention and motion estimation methods from bio-signal are important issues for accurate and safe interaction. This paper proposes a novel motion estimation sensor based on a geometrical muscle changes, and a motion estimation method using the sensor. For estimation of the motion, we measure the circumference change of the muscle which is proportional to muscle activation level using a flexible piezoelectric cable (pMAS, piezo muscle activation sensor), designed in band type. The pMAS measures variations of the cable band that originate from circumference changes of muscle bundles. Moreover, we estimate the elbow motion by applying the sensor to upper limb with least square method. The proposed sensor and prediction method are simple to use so that they can be used to motion prediction device and methods in rehabilitation and sports fields.

In this paper, we propose the organization of a sensor system and user's intent detection algorithm for walking assist rehabilitation robots. The main purpose of walking assist rehabilitation robots is assisting SCI patients to walk in normal environment. To use walking assist rehabilitation robot in normal environment, it is needed to consider various factors about user's safety and detection of user's intent and so on. For these purposes, we have analyzed the use case of rehabilitation robots and organized the system of sensors for walking assist rehabilitation robots and finally, we have developed the algorithm which is used to detect user's intent for those. We applied our proposal method in the rehabilitation robot, ROBIN, and verified their effectiveness by normal, not patient.

With the rising numbers of elderly and disabled people, the demand for welfare services using a robotic system and not involving human effort is likewise increasing. This study deals with a mobile-robot system combined with a BWS (Body Weight Support) system for gait rehabilitation. The BWS system is designed via the kinematic analysis of the robot's body-lifting characteristics and of the walking guide system that controls the total rehabilitation system integrated in the mobile robot. This mobile platform is operated by utilizing the AGV (Autonomous Guided Vehicle) driving algorithm. Especially, the method that integrates geometric path tracking and obstacle avoidance for a nonholonomic mobile robot is applied so that the system can be operated in an area where the elderly users are expected to be situated, such as in a public hospital or a rehabilitation center. The mobile robot follows the path by moving through the turning radius supplied by the pure-pursuit method which is one of the existing geometric path-tracking methods. The effectiveness of the proposed method is verified through the real experiments those are conducted for path tracking with static- and dynamic-obstacle avoidance. Finally, through the EMG (Electromyography) signal measurement of the subject, the performance of the proposed system in a real operation condition is evaluated.

The purpose of this study was to test the effectiveness of a prototype KAFO (Knee-Ankle-Foot Orthosis) powered by two artificial pneumatic muscles during walking. We had previously built powered AFO (Ankle-Foot Orthosis) and KO (Knee Orthosis) and used it effectively in studies on assistance of plantaflexion and knee extension motion. Extending the previous study to a KAFO presented additional challenges related to the assistance of gait motion for rehabilitation training. Five healthy males were performed gait motion on treadmill wearing KAFO equipped with artificial pneumatic muscles to power ankle plantaflexion and knee extension. Subjects walked on treadmill at 1.5 km/h under four conditions without extensive practice: 1) without wearing KAFO, 2) wearing KAFO with artificial muscles turned off, 3) wearing KAFO powered only in plantaflexion under feedforward control, and 4) wearing KAFO powered both in plantaflexion and knee extension under feedforward control. We collected surface electromyography, foot pressure and kinematics of ankle and knee joint. The experimental result showed that a muscular strength of wearing KAFO powered plnatarfexion and knee extension under feedforward control was measured to be lower due to pneumatic assistance and foot pressure of wearing KAFO powered plnatarfexion and knee extension under feedforward control was measured to be greater due to power assistance. In the result of motion analysis, the ankle angle of powered KAFO in terminal stance phase was found a peak value toward plantaflexion and there were difference of maximum knee flexion range among condition 2, 3 and 4 in mid-swing phase. The current orthosis design provided plantaflexion torque of ankle jonit in terminal stance phase and knee extension torque of knee joint in mid-swing phase.

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

This paper handles ZMP based control that is inspired by neural networks for humanoid robot walking on varying sloped surfaces. Humanoid robots are currently one of the most exciting research topics in the field of robotics, and maintaining stability while they are standing, walking or moving is a key concern. To ensure a steady and smooth walking gait of such robots, a feedforward type of neural network architecture, trained by the back propagation algorithm is employed. The inputs and outputs of the neural network architecture are the ZMPx and ZMPy errors of the robot, and the x, y positions of the robot, respectively. The neural network developed allows the controller to generate the desired balance of the robot positions, resulting in a steady gait for the robot as it moves around on a flat floor, and when it is descending slope. In this paper, experiments of humanoid robot walking are carried out, in which the actual position data from a prototype robot are measured in real time situations, and fed into a neural network inspired controller designed for stable bipedal walking.

In this paper, we propose a fault-tolerant controller for compensating measurement noise of feedback sensor. Because control systems operate via feedback sensor's signal, the measurement noise in sensor's signal results in performance degradation or even system failure. Therefore, control systems often demand on compensating measurement noise. Our controller is equipped with a compensator in order to reject or reduce the effect of measurement noise in feedback information. Our proposed method is verified via simulation and experiment for a Ball and Beam system.

Existing error compensation method of industrial electronic absolute displacement detector only depends on skilled engineers. This paper proposes a new table method in order to automatize error compensation. An waveform changes according to the parallel resistance for each pole were tabularized and four waveforms were superimposed to minimize total phase error. These process was verified using simulink. As a result of applying proposed method to the real sensor, peak to peak error was reduced from $3.428^{\circ}$ to $0.879^{\circ}$. In this case, compensation resistance is $4.7k\Omega$ in B pole and $20k\Omega$ in C pole. This compensation rate is comparable to skilled engineers, and it takes 0.8 second which is far shorter than 15 minutes when expert does.

In a target tracking system using image information from a CCD (Charged Couple Device) or an IIR (Imaging Infra-red) sensor, occluded targets can result in track losses. If the target is occlued by background objects such as buildings or trees, probability of track existence will be reduced sharply and track will be terminated due to track maintenance algorithms. This paper proposes data association algorithm based on target existence for the robust tracking performance. we suggest the HPDA (Highest Probability Data Association) algorithm based on target existence and the tracking performance is compared with the established method based on target perceivability. Image tracking simulation that utilizes virtual 3D images and real IR images is employed to evaluate the robustness of the proposed tracking algorithm.

In this paper, a new indoor positioning system based on incident angle measurement of infrared sensor has been suggested. Though there have been various researches on indoor positioning systems using vision sensor or ultrasonic sensor, they have not only advantages, but also disadvantages. In a new positioning system, there are three infrared emitters on fixed known positions. An incident angle sensor measures the angle differences between each two emitters. Mathematical problems to determine the position with angle differences and position information of emitters has been solved. Simulations and experiments have been implemented to show the performance of this new positioning system. The results of simulation were good. Since there existed problems of noise and signal conditioning, the experimented has been implemented in limited area. But the results were acceptable. This new positioning method can be applied to any indoor systems that need absolute position information.

Most motion control systems consist of a desired trajectory generator, a motion controller such as a conventional PID controller, and a plant to be controlled. The desired trajectory generator as well as the motion controller is very important to achieve a good tracking performance. Especially, if the desired trajectory is generated actively utilizing the maximum velocity, acceleration, jerk and snap as given system specifications, the tracking performance would be better. For this, we make use of the properties of convolution operator in order to generate a smooth (S-curve) trajectory satisfying the system specifications. Also, the proposed trajectory generation method is extended to more general cases with arbitrary initial and terminal conditions. In addition, the suggested trajectory generator can be easily realized for real-time implementation. Finally, the effectiveness of the suggested method is shown through numerical simulations.

The hot rolling process ranks the highest position for production in steel making process. The hot strip manufacturing processes consist of the reheating furnace, roughing and finishing mill and coiler. The reheating furnace heats the slab. The roughing and finishing mill produce the hot strip from slab. The hot strip quality mainly depends on finishing mill, which consists of 4-high 7 stands. The looper is installed between stands and is used for controlling the strip tension by the looper angle for better material flow. It is difficult to control the strip tension with the coupled looper system from interaction between the looper angle and strip tension. Too much deviation of strip tension severely affects the poor width quality of the hot strip. It is important to control simultaneously both the looper angle and strip tension with each of their target values. This paper proposes the fuzzy tension control, which is developed to minimize the width deviation of the hot strip by maintaining the proper strip tension between stands and to achieve the stable operation of the coupled looper system. The fuzzy tension control performance is compared with the conventional PID control by experimental results.

This paper proposes a systematic approach to the rate-based feedback control based on the supervisory control framework for discrete event systems. We design the supervisor to achieve the desired behavior for TCP wireless networks. From the analysis and simulation results, it is shown that the controlled networks guarantee the fair sharing of the available bandwidth and avoid the packet loss caused by the buffer overflow of TCP wireless networks.

The CAN (Controller Area Network) system is the most dominant protocol for in-vehicle networking system because it provides bounded transmission delay among ECUs (Electronic Control Units) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking system such as chassis network system because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions such as ADASs (Advanced Driver Assistance Systems) or IVISs (In-Vehicle Information Systems) require a network with more network capacity and the real-time QoS (Quality-of-Service). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents a distributed message allocation method that allocates messages to the more appropriate channel using forecast traffic of each channel. Finally, an experimental testbed using commercial off-the-shelf microcontrollers with two CAN protocol controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using the distributed message allocation method.